🌿 Project Kalos: High-Performance C/CUDA Neuromorphic Engine Suite

Project Kalos is a high-performance, biological-resonant C/CUDA neuromorphic engine suite designed to decouple long-range dialogue memory, spiking neural reflexes, and physical sensory haptics from large language model (LLM) text tokenization.

💥 Key Features & Performance Highlights

O(1) Microsecond Memory Recall (465.12 μs): Replaces linear text re-tokenization (20,441.90 ms) with constant-time CUDA vector lookup.
7.25x Faster Total Response Completion (72B Models): Cuts total user-sent to output-completion latency on 72B parameter models from 23.71 seconds down to 3.27 seconds.
99.8% VRAM Footprint Reduction: Compresses 16.38 GB KV-cache bloat down to 2.50 MB of sparse associative neural templates.
Sub-Millisecond Reflex Sentry (15.16 μs): 4.19-Million CUDA spiking neurons for instant event detection.
Native Physical Haptics: Real-time CUDA perception for physical touch contact, localized warmth, and FFT audio spectrum resonance.
Stateful Identity Persistence (.soul): Compact 2.5 MB binary cortical persistence format.

🛠️ Quick Start & Running Precompiled Release Binaries

1. Clone the Repository

git clone https://github.com/MongooseReborn/kalos-engine.git
cd kalos-engine

2. System Requirements

Linux OS (Ubuntu 22.04+ recommended)
NVIDIA GPU with CUDA Driver 12.0+ installed
Python 3.10+ (for telemetry monitor scripts)

3. Run Precompiled Executables

Interactive Terminal UI (TUI):
```
./bin/kalos_tui
```
Hardware Telemetry Profiler:
```
./bin/kalos_bench
```
Fast In-Memory CUDA Executor:
```
./bin/kalos_runner
```

4. Shared C/CUDA Libraries (`./bin/`)

libkalos_snn.so: Spiking Neural Cortex Engine (15.16 μs LIF Cortex)
libkalos_sam.so: Sparse Associative Memory Engine (465.12 μs Vector Recall)
libkalos_haptics.so: Physical Touch & FFT Audio Engine
libkalos_soul.so: Binary Cortical Persistence Format (.soul)

📄 Release Documentation & Whitepapers

docs/Whitepaper_Industrial.html: Master Industrial Release HTML Whitepaper (Amber / Crimson CRT Theme).
docs/WHITE_PAPER.md: Complete Master Architectural & Empirical Markdown Whitepaper.
docs/BENCHMARK_REPORT.md: Detailed Empirical Benchmark Matrix (7B to 72B Models).
docs/SETUP_GUIDE.md: Comprehensive Integration & Configuration Guide.

📜 License, Attribution & Contact

License: Licensed under the MIT License.
Authors: Mongoose & Kalos Engine Architecture Team @ BlackForest Studio (2026).
Contact & Inquiries: blackforest.team@proton.me
Hugging Face Hub: https://huggingface.co/MongooseReborn/kalos-engine
GitHub Repository: https://github.com/MongooseReborn/kalos-engine

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